In computer vision, an image typically includes one or more visible objects. Geometrical description of a visible object can be decomposed into two high-level ingredients: geometrical transformation and shape. Shape registration, which is also known as “shape alignment”, is commonly used to estimate both underlying object shape presentation and transform coefficients. In view of this, shape registration estimations are often used to facilitate image segmentation operations such as object localization and boundary detection. Image segmentation is a common component of many different applications of image analysis.
Unfortunately, existing shape registration techniques, many of which are based on parametric shape models (e.g., active contour, deformable, active shape models), often provide inconsistent and erroneous estimations of underlying shape presentation and corresponding geometrical transforms. As a result, image analysis applications, which depend on image segmentation to facilitate image analysis operations, are substantially limited in effect and accuracy. To make matters worse, conventional shape registration techniques typically rely on complex and computationally intense numerical optimization algorithms to estimate object shape and transform coefficients. This often makes it impractical or even impossible to implement image analysis applications that utilize shape registration on computing devices with limited processing and/or memory resources.